AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take.
In this course, you will learn:
- The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science
- What AI realistically can--and cannot--do
- How to spot opportunities to apply AI to problems in your own organization
- What it feels like to build machine learning and data science projects
- How to work with an AI team and build an AI strategy in your company
- How to navigate ethical and societal discussions surrounding AI
Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI.

Ministrado por

Andrew Ng

Transcrição

If you want to try your hand at an AI project, how do you select a worthwhile project to work on? Don't expect an idea to naturally come overnight. Sometimes it happens, but sometimes it also takes a few days or maybe a few weeks to come up with a worthy idea to pursue. In this video, you see a framework for brainstorming potentially exciting AI projects to pursue. Let's say you want to build an AI project for your business. You've already seen that AI can't do everything, and so there's going to be a certain set of things that is what AI can do. So let's let the circle represent the set of things that AI can do. Now, there's also going to be a certain set of things that is valuable for your business. So let's let this second circle represent a set of things that are valuable for your business. What you would like to do is try to select projects that are at the intersection of these two sets, so you select projects hopefully that are both feasible, that can be done with AI, and that are also valuable for your business. So AI experts will tend to have a good sense of what is and what isn't in the set on the left. And domain experts, experts in your business, be it sales and marketing, or agriculture or something else, will have the best sense of what is actually valuable for your business. So when brainstorming projects that AI can do and are valuable for your business, I will often bring together a team comprising both people knowledgeable of AI, as well as experts in your business area to brainstorm together. So that together they can try to identify projects at the intersection of both of these two sets. So sometimes we also call these cross-functional teams, and that just means a team that includes both AI experts, as well as domain experts, meaning experts in your area of business. When brainstorming projects, there's a framework that I've used with a lot of companies that I found to be useful. So let me share with you three principles or three ideas for how you can have a team brainstorm projects. First, even though there's been a lot of press coverage about AI automating jobs away, and this is an important societal issue that needs to addressed, when thinking about concrete AI projects, I find it much more useful to think about automating tasks rather than automating jobs. Take call center operations, there are a lot of tasks that happen in a call center. Ranging from people picking up the phone to answering phone calls to replying to emails, to taking specific actions, such as issuing a refund on behalf of a customer request. But of all of these tasks that employees in a call center do, there may be one, call routing or email routing, that maybe particularly amenable to machine learning automation. And it's by looking at all these tasks that the group of employees do and selecting one that may allow you to select the most fruitful project for automation in the near term. Let's look at another example, the job of a radiologist. There's been a lot of press about how AI my automate radiologists' jobs, but radiologists actually do a lot of things. They read x-rays, that's really important, but they also engage in their own continuing education. They consult with other doctors, they may mentor younger doctors, some of them also consult directly with patients. And so it's by looking at all of these tasks that a radiologist does that you may Identify one of them, let's say AI assistance or AI automation for reading x-rays, that allows you to select the most fruitful projects to work on. So what I would recommend is, if you look at your business, think about the tasks that people do, to see if you can identify just one of them, or just a couple of them, that may be automatable using machine learning. When I'm meeting CEOs of large companies to brainstorm AI projects for the company, a common question I'll also ask is, what are the main drivers of business value? And sometimes finding AI solutions or data science solutions to augment this can be very valuable. Finally, a third question that I've asked that sometimes let to valuable project ideas is, what are the main pain points in your business? Some of them could be solved with AI, some of them can't be solved with AI. But by understanding the main pain points in the business, that can create a useful starting point for brainstorming AI projects as well. I have one last piece of advice for brainstorming AI projects, which is that you can make progress even without big data, even without tons of data. Now don't get me wrong, having more data almost never hurts, other than maybe needing to pay a bit more for disk space or network bandwidth to transmit and store the data, having more data almost always is only helpful. And I love having lots of data. It is also true that data makes some on businesses, like web search, defensible. Web search is a long tail business, meaning that there are a lot of very, very rare web queries. And so seeing what people click on when they search on all of these rare web queries does help the leading web search engines have a much better search experience. So big data is great when you can get it, but I think big data also sometime over-hyped, and even with a small dataset, you can still often make progress. Here's an example, let's say you're building a automated visual inspection system for the coffee mug. So you want to automatically detect that the coffee mug on the right is defective. Well, if you had a million pictures of good coffee mugs and defective coffee mugs, it'd be great to have that many examples of pictures of good and bad coffee mugs to feed your AI system. But I hope you have not manufactured 1 million defective coffee mugs, because that feels like a very expensive thing to have to throw away. So sometimes with as few as 100, or maybe 1,000, or sometimes maybe as few as 10, you may be able to get started on the machine learning project. The amount of data you need is very problem dependent, and speaking with an AI engineer or AI expert would help you get better sense. There are some problems where 1,000 images may not be enough, where you do need big data to get good performance. But my advice is, don't give up just because you don't have a lot of data to start off with. And you can often still make progress, even with a small dataset. In this video, you saw a brainstorming framework, and a set of criteria for trying to come up with projects that hopefully can be doable with AI, and are also valuable for your business. Now, having brainstormed a list of projects, how do you select one or select a small handful to actually commit to and work on? Let's talk about that in the next video.